Fully-autonomous operation, including autonomous takeoff and landing, of helicopters and multi-rotor UAVs requires the implementation of a variety of sensory, communication, and processing capabilities. One way UAVs can autonomously take off and land autonomously is to start and finish missions at a known location or landing surfaces utilizing fixed markers for visual or sensory orientation.
In particular, takeoff and landing autonomously requires a precise estimation of the UAV pose (i.e., the three-dimensional position in space) in relation to a landing marker that cannot typically be accomplished with satellite-based navigation systems or other on-board sensors at the precision and framerate required by flight control systems.
Visual sensors can be successfully used during the landing process since they are able to provide the pose with an accuracy typically greater than GPS, sufficient to complete the autonomous landing task. However, vision data provide a considerable amount of information that must be processed. In fact, data provided by visual sensors have two main drawbacks: first, the computation time required to analyze and extract information from each frame reduces the rate at which the sensor can provide information; second, the computation time is typically dependent on the complexity of the image (frame) and on the number of operations that have to be performed. Therefore, providing a high-frequency pose estimation becomes mandatory for more precise localization and control performance especially during takeoff and landing, and a need exists for improved systems and methods to achieve such performance.